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Clustering noise-included data by controlling decision errors

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  • Hae-Sang Park
  • Jeonghwa Lee
  • Chi-Hyuck Jun

Abstract

Cluster analysis is an unsupervised learning technique for partitioning objects into several clusters. Assuming that noisy objects are included, we propose a soft clustering method which assigns objects that are significantly different from noise into one of the specified number of clusters by controlling decision errors through multiple testing. The parameters of the Gaussian mixture model are estimated from the EM algorithm. Using the estimated probability density function, we formulated a multiple hypothesis testing for the clustering problem, and the positive false discovery rate (pFDR) is calculated as our decision error. The proposed procedure classifies objects into significant data or noise simultaneously according to the specified target pFDR level. When applied to real and artificial data sets, it was able to control the target pFDR reasonably well, offering a satisfactory clustering performance. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Hae-Sang Park & Jeonghwa Lee & Chi-Hyuck Jun, 2014. "Clustering noise-included data by controlling decision errors," Annals of Operations Research, Springer, vol. 216(1), pages 129-144, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:129-144:10.1007/s10479-012-1238-7
    DOI: 10.1007/s10479-012-1238-7
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    References listed on IDEAS

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    1. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    2. Lian Duan & Lida Xu & Ying Liu & Jun Lee, 2009. "Cluster-based outlier detection," Annals of Operations Research, Springer, vol. 168(1), pages 151-168, April.
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